Advanced data analytics tools can help businesses uncover business-critical insights and gain a competitive edge. As customer experience analytics matures, it is becoming increasingly predictive and focused on personalization. A sophisticated data analytics tool can make predictions, or generate recommendations based on information gathered, thus giving contact center agents and bots more background and context on each individual customer they are servicing. In addition, analytics plays a significant role in increasing productivity. As one case in point, speech analytics can detect non-value adds, such as long conversational pauses or points in the conversation when the customer has to repeat responses. For these types of opportunities, managers and team leaders can provide agents coaching to educate them on where they have opportunities to be clearer in questions they ask customers.
Investments in analytics tools can reap significant benefits in numerous areas. Here are three major advantages of applying analytics in driving customer transformation:
- Analytics use Artificial Intelligence (AI) and Natural Language Processing (NLP) to help understand customer behavior and improve quality of customer interaction.Traditional business intelligence helps us convert data into information to understand patterns. New advancements like AI and NLP have stepped up customer experience. Today, talking to a bot is more and more akin to connecting with a human. Bots employ NLP for more colloquial verbiage and appropriate tone, recognition and understanding, to increasingly replicate a human interaction. Culture and customization can be applied to mimic character and persona of human agents. AI and bots also enable real-time customization of customer experience using their complex self-learning capability. They evolve with each customer interaction becoming smarter and more intuitive about customer preferences. With this AI and NLP innovation, interactive analytics are used to analyze customer conversation tone and context. At key focus is the interaction between the customer and agent by channel, whether it’s phone, email, chat, or social media. Analytics create better understanding of the quality of the interaction between customer and agent. As a feedback mechanism, analytics can relay to brands the effectiveness of their CX talent.
- Predictive and prescriptive analytics can drive CX transformation through product recommendations. Predictive analytics study customer history and behavior patterns. Based on customer behavior data points, predictive analytics shed light on patterns and possible occurrences in customers’ browsing and shopping behavior. What’s more, these analytics also help take preemptive measures required to take on upcoming challenges. For example, brands have increasingly been using consumer classification, which ensures customers receive product recommendations based on their buying choices, preferences, and habits. Similarly, healthcare entities have started to apply data analytics to classify patients in different personality types, such as “follower” or “health-nut.” This can help healthcare entities to personalize the level of interaction that can be provided to patients and thus improve patient engagement. Purchasing behavior prediction can be as simple as understanding that a customer who has recently bought a car will eventually be looking to buy accessories. In this case, a brand can recommend accessories and services, possibly at a lower cost, for a minimal effort, yet generate a major boost to sales, revenue, and customer satisfaction. Driven by good predictive business insights, an intelligent recommendation engine can spike both sales and customer satisfaction (CSAT) increases. Used proactively, analytics can set in place the steps and remedies required to handle future business or product challenges. Analytics, for instance, are essential to planning for stock inventory, transport logistics, and customer support—all key contributors to better CSAT. For example, advanced analytics can be deployed to map a customer call to the work queue of a customer service representative (CSR), who can handle the request in the best possible manner and within the shortest time. The algorithm that determines this match is based on historical data on the CSR’s interactions on various service lines and the customer’s past interactions on various product lines, creating a matrix of probable scenarios, a few of them being the most optimal.
- For optimized CX, analytics ensure better agent coaching and skills reinforcement.Analytics helps reinforce agents with good interactions. Conversely, analytics can be used to correct agents in areas where they need improvement. Interactive analytics provides better customer-agent interactions as a result of positive reinforcement and highly personalized agent development. Another advantage of interaction analytics is that conversations are analyzed by channel, which helps sort agent performances by the specialized set of skills and knowledge required by that channel. Every interaction channel has its own set of requirements, and agents need to be coached on those parameters while enhancing or correcting their behavior on that channel. Additionally, interaction analytics provide information on the overall team’s performance. This helps ensure more comprehensive coaching and skills training, that is focused on the entire team’s performance (positive and negative). Benchmarking is an especially useful training technique, as it will help guide an agent on the areas in which he or she needs to improve. A call driver pattern analysis, especially in high call volume scenarios, can be used to discover call trends by day/week and accordingly do a skill mapping. On the training front, advanced learning platforms apply analytics and data sciences to analyze past training records of associates, data on previous engagements, job roles, span, and behavior—all to arrive at custom training programs for agents. This training helps agents hone their competencies and skills, to equip them to perform their roles better.
It’s still in the early stages for how analytics can affect customer transformation. We have yet to perfect the science of interpreting data, to understand its underlying message and explain it simply. The field has high potential and will be home to many innovations in the years to come. What will be critical is our ability to apply the information in a meaningful and sustainable fashion. Physicist, Albert Einstein aptly summarizes some key thoughts on these changes: “Not everything that can be counted counts, and not everything that counts can be counted.” and “If you can’t explain it simply, you don’t understand it well enough.”
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